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The paper introduces Temporally Unified Adversarial Perturbations (TUAPs) to address the issue of temporally inconsistent adversarial attacks in time series forecasting. To generate TUAPs, the authors propose a Timestamp-wise Gradient Accumulation Method (TGAM) that enforces temporal unification by aggregating local gradient information from overlapping samples. Experiments on benchmark datasets demonstrate that TUAPs, generated using TGAM, outperform existing methods in both white-box and black-box transfer attack scenarios, even without temporal unification constraints.
Time series forecasting models are more vulnerable than we thought: enforcing temporal consistency in adversarial attacks reveals significantly stronger vulnerabilities than previously observed.
While deep learning models have achieved remarkable success in time series forecasting, their vulnerability to adversarial examples remains a critical security concern. However, existing attack methods in the forecasting field typically ignore the temporal consistency inherent in time series data, leading to divergent and contradictory perturbation values for the same timestamp across overlapping samples. This temporally inconsistent perturbations problem renders adversarial attacks impractical for real-world data manipulation. To address this, we introduce Temporally Unified Adversarial Perturbations (TUAPs), which enforce a temporal unification constraint to ensure identical perturbations for each timestamp across all overlapping samples. Moreover, we propose a novel Timestamp-wise Gradient Accumulation Method (TGAM) that provides a modular and efficient approach to effectively generate TUAPs by aggregating local gradient information from overlapping samples. By integrating TGAM with momentum-based attack algorithms, we ensure strict temporal consistency while fully utilizing series-level gradient information to explore the adversarial perturbation space. Comprehensive experiments on three benchmark datasets and four representative state-of-the-art models demonstrate that our proposed method significantly outperforms baselines in both white-box and black-box transfer attack scenarios under TUAP constraints. Moreover, our method also exhibits superior transfer attack performance even without TUAP constraints, demonstrating its effectiveness and superiority in generating adversarial perturbations for time series forecasting models.